Lipid components of flax, perilla, and chia seeds
Bibliographic record
Abstract
Abstract Composition of fatty acids, tocopherols, sterols, and TAGs in the lipids of flax, perilla, and chia seeds were investigated where lipid content was at 45, 40, and 35%, respectively. α‐Linolenic acid (ALA) dominated among fatty acids in all oils and accounted for 58.2, 60.9, and 59.8% in flax, perilla, and chia, correspondingly in these three oils trilinolenin was the main TAG found at 19.7, 22.6, and 21.3%. Triunsaturated TAGs accounted for 77.9, 77.5, and 74.5% of the total amounts in flax, perilla, and chia oils. Contents of tocopherol were at 747 in flax, 734 in perilla, and 446 mg/kg in chia seed lipids. γ‐Tocopherol was the dominating isomer contributing 72.7% in flax, 94.3% in perilla, and 94.4% in chia to the total amount of tocopherols. Flaxseed lipids contained 25.6% of plastochromanol‐8, derivative of γ‐tocotrienol with longer side chain; perilla and chia oils contained only 1.4% of it. Phytosterols were present at 4072, 4606, and 4132 mg/kg in those seeds, respectively. Among sterols, β‐sitosterol dominated and was found at 35.6, 73.3, and 49.8% of the total amounts of sterols in flax, perilla, and chia seed lipids. All of the investigated oilseeds have an excellent nutritional quality and can be a potential source of nutraceutical fats which can enrich diet in linolenic acid and other functional components.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".